Abstract
The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.
Highlights
The translational axis is one of the most important subsystems in modern machine tools, which has been widely used for transforming rotational motion into linear motion, due to its advantages of low sensitivity to inertia variations, good positional accuracy and high driving speeds [1]
A biphase randomization wavelet bicoherence (BRWB) based quadratic nonlinearity feature is established for translational axis condition monitoring of the machine tools, which shows that it is possible to perform condition monitoring by using servomotor torque signature
Some conclusions are drawn as follows: (1) A BRWB is established to overcome the problem of current wavelet bicoherence (WB), which can eliminate the spurious peaks coming from long coherence time waves and non-quadratic phase coupling (QPC) waves efficiently
Summary
The translational axis is one of the most important subsystems in modern machine tools, which has been widely used for transforming rotational motion into linear motion, due to its advantages of low sensitivity to inertia variations, good positional accuracy and high driving speeds [1]. Starting from a new condition, each part of the translational axis system is properly manufactured and installed into the system without any errors, and is operated at a particular level of performance As their service life progresses, faults such as material fatigue, abrasion or adhesion in the components always leads to a performance degradation, which means the parts can no longer meet their original service requirements [2]. A wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. The quadratic nonlinearity of the servomotor torque signature is discussed, and a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis.
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